CategoricalDistributions.jl

Probability distributions and measures for finite sample spaces whose elements are labeled (consist of the class pool of a CategoricalArray).

Designed for performance in machine learning applications. For example, probabilistic classifiers in MLJ typically predict the UnivariateFiniteVector objects defined in this package.

For probability distributions over integers see the Distributions.jl package, whose methods the current package extends.

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Installation

using Pkg
Pkg.add("CategoricalDistributions")

Basic usage

The sample space of the UnivariateFinite distributions provided by this package is the class pool of a CategoricalArray:

using CategoricalDistributions
using CategoricalArrays
import Distributions
import UnicodePlots # for optional pretty display
data = ["no", "yes", "no", "maybe", "maybe", "no",
       "maybe", "no", "maybe"] |> categorical
julia> d = Distributions.fit(UnivariateFinite, data)
               UnivariateFinite{Multiclass{3}}
         ┌                                        ┐
   maybe ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.4
      no ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.5
     yes ┤■■■■■■■ 0.1
         └                                        ┘
julia> pdf(d, "no")
0.5

julia> mode(d)
CategoricalValue{String, UInt32} "no"

A UnivariateFinite distribution can also be constructed directly from a probability vector:

julia> d2 = UnivariateFinite(["no", "yes"], [0.15, 0.85], pool=data)
             UnivariateFinite{Multiclass{3}}
       ┌                                        ┐
    no ┤■■■■■■ 0.15
   yes ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.85
       └                                        ┘

A UnivariateFinite distribution tracks all classes in the pool:

levels(d2)
3-element Vector{String}:
 "maybe"
 "no"
 "yes"

julia> pdf(d2, "maybe")
0.0

julia> pdf(d2, "okay")
ERROR: DomainError with Value okay not in pool. :

Arrays of UnivariateFinite distributions are defined using the same constructor. Broadcasting methods, such as pdf, are optimized for such arrays:

julia> v = UnivariateFinite(["no", "yes"], [0.1, 0.2, 0.3, 0.4], augment=true, pool=data)
4-element UnivariateFiniteArray{Multiclass{3}, String, UInt32, Float64, 1}:
 UnivariateFinite{Multiclass{3}}(no=>0.9, yes=>0.1)
 UnivariateFinite{Multiclass{3}}(no=>0.8, yes=>0.2)
 UnivariateFinite{Multiclass{3}}(no=>0.7, yes=>0.3)
 UnivariateFinite{Multiclass{3}}(no=>0.6, yes=>0.4)

julia> pdf.(v, "no")
4-element Vector{Float64}:
 0.9
 0.8
 0.7
 0.6

Query the UnivariateFinite doc-string for advanced constructor options.

A (non-standard) implementation of pdf allows for extraction of the full probability array:

julia> L = levels(data)
3-element Vector{String}:
 "maybe"
 "no"
 "yes"

julia> pdf(v, L)
4×3 Matrix{Float64}:
 0.0  0.9  0.1
 0.0  0.8  0.2
 0.0  0.7  0.3
 0.0  0.6  0.4

Measures over finite labeled sets

There is, in fact, no enforcement that probabilities in a UnivariateFinite distribution sum to one, only that they be belong to a type T for which zero(T) is defined. In particular UnivariateFinite objects implement arbitrary non-negative, signed, or complex measures over a finite labeled set.

What does this package provide?

• A new type UnivariateFinite{S} for representing probability distributions over the pool of a CategoricalArray, that is, over finite labeled sets. Here S is a subtype of OrderedFactor from ScientificTypesBase.jl, if the pool is ordered, or of Multiclass if the pool is unordered.

• A new array type UnivariateFiniteArray{S} <: AbstractArray{<:UnivariateFinite{S}} for efficiently manipulating arrays of UnivariateFinite distributions.

• Implementations of rand for generating random samples of a UnivariateFinite distribution.

• Implementations of the pdf, logpdf, mode and modes methods of Distributions.jl, with efficient broadcasting over the new array type.

• Implementation of Distributions.fit from Distributions.jl for UnivariateFinite distributions.

• A single constructor for constructing UnivariateFinite distributions and arrays thereof, from arrays of probabilities.

Acknowledgements

The initial release of this package is based almost entirely on code originally residing in MLJBase.jl with contributions from Anthony Blaom, Thibaut Lienart, Samuel Okon, and Chad Scherrer. These contributions are not reflected in the current repository's commit history.